{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Feature.xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Features.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_chousui</th>\n",
       "      <th>SVAI_kaihua</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509428</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456349</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466787</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479437</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438036</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537977</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511967</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380461</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  SVAI_chousui  SVAI_kaihua   \n",
       "0                            290.517675  ...      0.509428     0.502203  \\\n",
       "1                            290.784441  ...      0.456349     0.444113   \n",
       "2                            290.271875  ...      0.375751     0.406768   \n",
       "3                            290.136585  ...      0.466787     0.392344   \n",
       "4                            291.813767  ...      0.479437     0.456340   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.438036     0.427608   \n",
       "98                           292.107970  ...      0.537977     0.518776   \n",
       "99                           290.932713  ...      0.511967     0.563379   \n",
       "100                          290.356689  ...      0.480808     0.460888   \n",
       "101                          291.427115  ...      0.380461     0.390789   \n",
       "\n",
       "     WDRVI_bajie  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0      -0.270294       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1      -0.220879       0.020097    0.272293     0.321097      0.313054   \n",
       "2      -0.273682      -0.176015    0.255247     0.291260      0.276982   \n",
       "3      -0.305916       0.009030    0.277992     0.313744      0.335973   \n",
       "4      -0.155182       0.027083    0.329961     0.356682      0.342203   \n",
       "..           ...            ...         ...          ...           ...   \n",
       "97     -0.222630      -0.033628    0.276777     0.307460      0.306785   \n",
       "98     -0.049587       0.143995    0.356591     0.372768      0.380640   \n",
       "99      0.082507       0.149694    0.389395     0.347135      0.345173   \n",
       "100    -0.102704       0.005744    0.309025     0.317261      0.348503   \n",
       "101    -0.356993      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.062063  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'soil_temperature_level_2_kaihua', 'soil_temperature_level_3_kaihua', 'SoilMoi0_10cm_inst_kaihua', 'SoilMoi10_40cm_inst_kaihua',\n",
    "                  'DVI_kaihua', 'EVI_kaihua', 'EVI2_kaihua', 'GCVI_kaihua', 'Lai_500m_kaihua', 'MCARI_kaihua', 'OSAVI_kaihua', \n",
    "                  'SR_kaihua', 'SVAI_kaihua', 'WDVI_kaihua', 'wet_kaihua'])\n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'soil_temperature_level_2_kaihua', 'soil_temperature_level_3_kaihua', 'SoilMoi0_10cm_inst_kaihua', 'SoilMoi10_40cm_inst_kaihua',\n",
    "                  'DVI_kaihua', 'EVI_kaihua', 'EVI2_kaihua', 'GCVI_kaihua', 'Lai_500m_kaihua', 'MCARI_kaihua', 'OSAVI_kaihua', \n",
    "                  'SR_kaihua', 'SVAI_kaihua', 'WDVI_kaihua', 'wet_kaihua'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#d=d.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])\n",
    "#dd=dd.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.087168</td>\n",
       "      <td>544.950758</td>\n",
       "      <td>671.976305</td>\n",
       "      <td>0.749379</td>\n",
       "      <td>0.77441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）     解释方差\n",
       "xgboost      0.087168   544.950758   671.976305   0.749379  0.77441"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>7201.994141</td>\n",
       "      <td>-3.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>6074.051758</td>\n",
       "      <td>1.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>5573.432617</td>\n",
       "      <td>-2.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7020.171387</td>\n",
       "      <td>-11.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>6605.979492</td>\n",
       "      <td>-13.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>6851.231445</td>\n",
       "      <td>3.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>7072.257812</td>\n",
       "      <td>-6.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>6162.890625</td>\n",
       "      <td>7.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>5813.433105</td>\n",
       "      <td>6.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>5482.086426</td>\n",
       "      <td>-17.40%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred  precent\n",
       "0    7467.000138  7201.994141   -3.55%\n",
       "1    5972.666001  6074.051758    1.70%\n",
       "2    5687.791498  5573.432617   -2.01%\n",
       "3    7959.597222  7020.171387  -11.80%\n",
       "4    7662.496889  6605.979492  -13.79%\n",
       "..           ...          ...      ...\n",
       "97   6613.422500  6851.231445    3.60%\n",
       "98   7579.469809  7072.257812   -6.69%\n",
       "99   5754.140236  6162.890625    7.10%\n",
       "100  5467.128359  5813.433105    6.33%\n",
       "101  6637.200003  5482.086426  -17.40%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (Bo-XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(1000, 10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
